人脸生成(Face Generation)

在该项目中,你将使用生成式对抗网络(Generative Adversarial Nets)来生成新的人脸图像。

获取数据

该项目将使用以下数据集:

  • MNIST
  • CelebA

由于 CelebA 数据集比较复杂,而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型,以让你更快的评估所建立模型的性能。

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
# data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

探索数据(Explore the Data)

MNIST

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
/usr/local/lib/python3.5/site-packages/matplotlib/font_manager.py:280: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  'Matplotlib is building the font cache using fc-list. '
Out[2]:
<matplotlib.image.AxesImage at 0x7fc252b408d0>

CelebA

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸,不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fc252a419e8>

预处理数据(Preprocess the Data)

由于该项目的重点是建立 GANs 模型,我们将为你预处理数据。

经过数据预处理,MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分,然后调整到 28x28 维度。

MNIST 数据集中的图像是单通道的黑白图像,CelebA 数据集中的图像是 三通道的 RGB 彩色图像

建立神经网络(Build the Neural Network)

你将通过部署以下函数来建立 GANs 的主要组成部分:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

检查 TensorFlow 版本并获取 GPU 型号

检查你是否使用正确的 TensorFlow 版本,并获取 GPU 型号

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

输入(Input)

部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符:

  • 输入图像占位符: 使用 image_widthimage_heightimage_channels 设置为 rank 4。
  • 输入 Z 占位符: 设置为 rank 2,并命名为 z_dim
  • 学习速率占位符: 设置为 rank 0。

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, (None))

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

辨别器(Discriminator)

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    keep_prob = 0.8
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn1 = tf.layers.batch_normalization(x1, training=True)
        relu1 = tf.maximum(alpha*bn1, bn1)
        drop1 = tf.nn.dropout(relu1, keep_prob=keep_prob)
        
        x2 = tf.layers.conv2d(drop1, 128, 5, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha*bn2, bn2)
        drop2 = tf.nn.dropout(relu2, keep_prob=keep_prob)
        
        x3 = tf.layers.conv2d(drop2, 256, 5, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha*bn3, bn3)
        drop3 = tf.nn.dropout(relu3, keep_prob=keep_prob)
        
        flat = tf.reshape(drop3, (-1,4*4*256))
        logits= tf.layers.dense(flat,1)
        out= tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

生成器(Generator)

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    reuse = not is_train
    alpha = 0.2
    keep_prob = 0.8
    with tf.variable_scope('generator', reuse=reuse):
        x1 = tf.layers.dense(z, 4*4*512)
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        x1 = tf.nn.dropout(x1, keep_prob=keep_prob)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid',
                                        kernel_initializer=tf.contrib.layers.xavier_initializer())
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        x2 = tf.nn.dropout(x2, keep_prob=keep_prob)

        
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 4, strides=2, padding="same",
                                       kernel_initializer=tf.contrib.layers.xavier_initializer())
        x3 = tf.layers.batch_normalization(x3,training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        x3 = tf.nn.dropout(x3, keep_prob=keep_prob)

    
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 4, strides=2, padding='same',
                                           kernel_initializer=tf.contrib.layers.xavier_initializer())
        
        out = tf.tanh(logits)
        
    return out
    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

损失函数(Loss)

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

使用你已实现的函数:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = 0.1
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss
    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

优化(Optimization)

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminatorgenerator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt
    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

训练神经网络(Neural Network Training)

输出显示

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出,这会帮你评估 GANs 模型的训练程度。

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

训练

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

注意:在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    input_real, input_z, lr = model_inputs(*data_shape[1:4], z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    show_every=100
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images = batch_images.reshape(batch_size, *data_shape[1:4])
                batch_images = batch_images * 2
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z})

                if steps % show_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    show_generator_output(sess, 32, input_z, data_shape[3], data_image_mode)

MNIST

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代,GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失,或接近 0。

In [13]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 Step 100... Discriminator Loss: 1.5035... Generator Loss: 0.7239
Epoch 1/2 Step 200... Discriminator Loss: 1.4886... Generator Loss: 0.7057
Epoch 1/2 Step 300... Discriminator Loss: 1.4252... Generator Loss: 0.8825
Epoch 1/2 Step 400... Discriminator Loss: 1.3522... Generator Loss: 0.8092
Epoch 1/2 Step 500... Discriminator Loss: 1.3959... Generator Loss: 0.6734
Epoch 1/2 Step 600... Discriminator Loss: 1.4123... Generator Loss: 0.7189
Epoch 1/2 Step 700... Discriminator Loss: 1.6348... Generator Loss: 1.7092
Epoch 1/2 Step 800... Discriminator Loss: 1.4308... Generator Loss: 0.6462
Epoch 1/2 Step 900... Discriminator Loss: 1.4968... Generator Loss: 1.0302
Epoch 2/2 Step 1000... Discriminator Loss: 1.2767... Generator Loss: 0.6985
Epoch 2/2 Step 1100... Discriminator Loss: 1.3796... Generator Loss: 0.5293
Epoch 2/2 Step 1200... Discriminator Loss: 1.2761... Generator Loss: 1.6625
Epoch 2/2 Step 1300... Discriminator Loss: 1.6992... Generator Loss: 0.4136
Epoch 2/2 Step 1400... Discriminator Loss: 1.0366... Generator Loss: 1.1683
Epoch 2/2 Step 1500... Discriminator Loss: 1.0463... Generator Loss: 1.0494
Epoch 2/2 Step 1600... Discriminator Loss: 1.7949... Generator Loss: 0.4051
Epoch 2/2 Step 1700... Discriminator Loss: 0.8908... Generator Loss: 1.4912
Epoch 2/2 Step 1800... Discriminator Loss: 1.2493... Generator Loss: 1.5437

CelebA

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代,或者当 GANs 开始产生真实人脸图像时停止它。

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.45


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 25

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/25 Step 100... Discriminator Loss: 1.8962... Generator Loss: 0.5486
Epoch 1/25 Step 200... Discriminator Loss: 1.6795... Generator Loss: 0.6148
Epoch 1/25 Step 300... Discriminator Loss: 1.4653... Generator Loss: 0.7233
Epoch 1/25 Step 400... Discriminator Loss: 1.6193... Generator Loss: 0.9799
Epoch 1/25 Step 500... Discriminator Loss: 1.5573... Generator Loss: 0.6956
Epoch 1/25 Step 600... Discriminator Loss: 1.4618... Generator Loss: 0.7358
Epoch 1/25 Step 700... Discriminator Loss: 1.5496... Generator Loss: 0.7815
Epoch 1/25 Step 800... Discriminator Loss: 1.4569... Generator Loss: 0.7218
Epoch 1/25 Step 900... Discriminator Loss: 1.3146... Generator Loss: 0.8311
Epoch 1/25 Step 1000... Discriminator Loss: 1.4874... Generator Loss: 0.8315
Epoch 1/25 Step 1100... Discriminator Loss: 1.4475... Generator Loss: 0.7320
Epoch 1/25 Step 1200... Discriminator Loss: 1.5656... Generator Loss: 0.6186
Epoch 1/25 Step 1300... Discriminator Loss: 1.4614... Generator Loss: 0.7139
Epoch 1/25 Step 1400... Discriminator Loss: 1.4133... Generator Loss: 0.8844
Epoch 1/25 Step 1500... Discriminator Loss: 1.4261... Generator Loss: 0.6640
Epoch 1/25 Step 1600... Discriminator Loss: 1.4863... Generator Loss: 0.7056
Epoch 1/25 Step 1700... Discriminator Loss: 1.5674... Generator Loss: 0.7603
Epoch 1/25 Step 1800... Discriminator Loss: 1.5112... Generator Loss: 0.6438
Epoch 1/25 Step 1900... Discriminator Loss: 1.4460... Generator Loss: 0.8932
Epoch 1/25 Step 2000... Discriminator Loss: 1.5986... Generator Loss: 0.4914
Epoch 1/25 Step 2100... Discriminator Loss: 1.4302... Generator Loss: 0.6928
Epoch 1/25 Step 2200... Discriminator Loss: 1.4043... Generator Loss: 0.7622
Epoch 1/25 Step 2300... Discriminator Loss: 1.4896... Generator Loss: 0.9031
Epoch 1/25 Step 2400... Discriminator Loss: 1.4451... Generator Loss: 0.8688
Epoch 1/25 Step 2500... Discriminator Loss: 1.4514... Generator Loss: 0.6979
Epoch 1/25 Step 2600... Discriminator Loss: 1.4226... Generator Loss: 0.7258
Epoch 1/25 Step 2700... Discriminator Loss: 1.4513... Generator Loss: 0.6834
Epoch 1/25 Step 2800... Discriminator Loss: 1.4434... Generator Loss: 0.7375
Epoch 1/25 Step 2900... Discriminator Loss: 1.4492... Generator Loss: 0.6292
Epoch 1/25 Step 3000... Discriminator Loss: 1.4391... Generator Loss: 0.7513
Epoch 1/25 Step 3100... Discriminator Loss: 1.5202... Generator Loss: 0.6538
Epoch 1/25 Step 3200... Discriminator Loss: 1.4207... Generator Loss: 0.6390
Epoch 1/25 Step 3300... Discriminator Loss: 1.4286... Generator Loss: 0.6619
Epoch 1/25 Step 3400... Discriminator Loss: 1.3874... Generator Loss: 0.8108
Epoch 1/25 Step 3500... Discriminator Loss: 1.4003... Generator Loss: 0.7697
Epoch 1/25 Step 3600... Discriminator Loss: 1.5086... Generator Loss: 0.8274
Epoch 1/25 Step 3700... Discriminator Loss: 1.4057... Generator Loss: 0.7984
Epoch 1/25 Step 3800... Discriminator Loss: 1.3971... Generator Loss: 0.7921
Epoch 1/25 Step 3900... Discriminator Loss: 1.4264... Generator Loss: 0.7481
Epoch 1/25 Step 4000... Discriminator Loss: 1.3769... Generator Loss: 0.7290
Epoch 1/25 Step 4100... Discriminator Loss: 1.4473... Generator Loss: 0.7859
Epoch 1/25 Step 4200... Discriminator Loss: 1.4368... Generator Loss: 0.8109
Epoch 1/25 Step 4300... Discriminator Loss: 1.4179... Generator Loss: 0.7547
Epoch 1/25 Step 4400... Discriminator Loss: 1.3992... Generator Loss: 0.8419
Epoch 1/25 Step 4500... Discriminator Loss: 1.4006... Generator Loss: 0.8122
Epoch 1/25 Step 4600... Discriminator Loss: 1.3943... Generator Loss: 0.8478
Epoch 1/25 Step 4700... Discriminator Loss: 1.4190... Generator Loss: 0.7130
Epoch 1/25 Step 4800... Discriminator Loss: 1.3859... Generator Loss: 0.7249
Epoch 1/25 Step 4900... Discriminator Loss: 1.4200... Generator Loss: 0.7474
Epoch 1/25 Step 5000... Discriminator Loss: 1.4227... Generator Loss: 0.6905
Epoch 1/25 Step 5100... Discriminator Loss: 1.4309... Generator Loss: 0.8142
Epoch 1/25 Step 5200... Discriminator Loss: 1.3789... Generator Loss: 0.8451
Epoch 1/25 Step 5300... Discriminator Loss: 1.4213... Generator Loss: 0.6765
Epoch 1/25 Step 5400... Discriminator Loss: 1.3755... Generator Loss: 0.7929
Epoch 1/25 Step 5500... Discriminator Loss: 1.3756... Generator Loss: 0.7552
Epoch 1/25 Step 5600... Discriminator Loss: 1.4375... Generator Loss: 0.7426
Epoch 1/25 Step 5700... Discriminator Loss: 1.4554... Generator Loss: 0.7035
Epoch 1/25 Step 5800... Discriminator Loss: 1.3600... Generator Loss: 0.8139
Epoch 1/25 Step 5900... Discriminator Loss: 1.4429... Generator Loss: 0.7145
Epoch 1/25 Step 6000... Discriminator Loss: 1.4060... Generator Loss: 0.7997
Epoch 1/25 Step 6100... Discriminator Loss: 1.3770... Generator Loss: 0.8571
Epoch 1/25 Step 6200... Discriminator Loss: 1.4300... Generator Loss: 0.7379
Epoch 1/25 Step 6300... Discriminator Loss: 1.3673... Generator Loss: 0.7570
Epoch 2/25 Step 6400... Discriminator Loss: 1.4170... Generator Loss: 0.8130
Epoch 2/25 Step 6500... Discriminator Loss: 1.4180... Generator Loss: 0.7639
Epoch 2/25 Step 6600... Discriminator Loss: 1.3709... Generator Loss: 0.7454
Epoch 2/25 Step 6700... Discriminator Loss: 1.3249... Generator Loss: 0.7456
Epoch 2/25 Step 6800... Discriminator Loss: 1.3933... Generator Loss: 0.8177
Epoch 2/25 Step 6900... Discriminator Loss: 1.4154... Generator Loss: 0.8415
Epoch 2/25 Step 7000... Discriminator Loss: 1.4115... Generator Loss: 0.7329
Epoch 2/25 Step 7100... Discriminator Loss: 1.3920... Generator Loss: 0.7008
Epoch 2/25 Step 7200... Discriminator Loss: 1.4009... Generator Loss: 0.7831
Epoch 2/25 Step 7300... Discriminator Loss: 1.3795... Generator Loss: 0.7913
Epoch 2/25 Step 7400... Discriminator Loss: 1.4255... Generator Loss: 0.7226
Epoch 2/25 Step 7500... Discriminator Loss: 1.4227... Generator Loss: 0.7600
Epoch 2/25 Step 7600... Discriminator Loss: 1.3651... Generator Loss: 0.8037
Epoch 2/25 Step 7700... Discriminator Loss: 1.3985... Generator Loss: 0.8056
Epoch 2/25 Step 7800... Discriminator Loss: 1.3509... Generator Loss: 0.7699
Epoch 2/25 Step 7900... Discriminator Loss: 1.3969... Generator Loss: 0.7639
Epoch 2/25 Step 8000... Discriminator Loss: 1.3741... Generator Loss: 0.7794
Epoch 2/25 Step 8100... Discriminator Loss: 1.3819... Generator Loss: 0.7433
Epoch 2/25 Step 8200... Discriminator Loss: 1.3751... Generator Loss: 0.7849
Epoch 2/25 Step 8300... Discriminator Loss: 1.4242... Generator Loss: 0.7987
Epoch 2/25 Step 8400... Discriminator Loss: 1.3920... Generator Loss: 0.7972
Epoch 2/25 Step 8500... Discriminator Loss: 1.3681... Generator Loss: 0.7934
Epoch 2/25 Step 8600... Discriminator Loss: 1.4022... Generator Loss: 0.7460
Epoch 2/25 Step 8700... Discriminator Loss: 1.3573... Generator Loss: 0.7889
Epoch 2/25 Step 8800... Discriminator Loss: 1.4398... Generator Loss: 0.7470
Epoch 2/25 Step 8900... Discriminator Loss: 1.3656... Generator Loss: 0.8050
Epoch 2/25 Step 9000... Discriminator Loss: 1.3732... Generator Loss: 0.8095
Epoch 2/25 Step 9100... Discriminator Loss: 1.3729... Generator Loss: 0.7998
Epoch 2/25 Step 9200... Discriminator Loss: 1.3415... Generator Loss: 0.8193
Epoch 2/25 Step 9300... Discriminator Loss: 1.3789... Generator Loss: 0.7815
Epoch 2/25 Step 9400... Discriminator Loss: 1.3142... Generator Loss: 0.8032
Epoch 2/25 Step 9500... Discriminator Loss: 1.3948... Generator Loss: 0.7877
Epoch 2/25 Step 9600... Discriminator Loss: 1.3878... Generator Loss: 0.7615
Epoch 2/25 Step 9700... Discriminator Loss: 1.3461... Generator Loss: 0.7647
Epoch 2/25 Step 9800... Discriminator Loss: 1.4062... Generator Loss: 0.6965
Epoch 2/25 Step 9900... Discriminator Loss: 1.3232... Generator Loss: 0.7742
Epoch 2/25 Step 10000... Discriminator Loss: 1.3525... Generator Loss: 0.7603
Epoch 2/25 Step 10100... Discriminator Loss: 1.3941... Generator Loss: 0.7378
Epoch 2/25 Step 10200... Discriminator Loss: 1.3872... Generator Loss: 0.7055
Epoch 2/25 Step 10300... Discriminator Loss: 1.3881... Generator Loss: 0.7870
Epoch 2/25 Step 10400... Discriminator Loss: 1.3555... Generator Loss: 0.7833
Epoch 2/25 Step 10500... Discriminator Loss: 1.2704... Generator Loss: 0.8555
Epoch 2/25 Step 10600... Discriminator Loss: 1.3649... Generator Loss: 0.8274
Epoch 2/25 Step 10700... Discriminator Loss: 1.3330... Generator Loss: 0.8615
Epoch 2/25 Step 10800... Discriminator Loss: 1.3370... Generator Loss: 0.7907
Epoch 2/25 Step 10900... Discriminator Loss: 1.3971... Generator Loss: 0.7428
Epoch 2/25 Step 11000... Discriminator Loss: 1.4236... Generator Loss: 0.7251
Epoch 2/25 Step 11100... Discriminator Loss: 1.3743... Generator Loss: 0.8400
Epoch 2/25 Step 11200... Discriminator Loss: 1.2789... Generator Loss: 0.8729
Epoch 2/25 Step 11300... Discriminator Loss: 1.3418... Generator Loss: 0.7843
Epoch 2/25 Step 11400... Discriminator Loss: 1.3488... Generator Loss: 0.8961
Epoch 2/25 Step 11500... Discriminator Loss: 1.3207... Generator Loss: 0.8340
Epoch 2/25 Step 11600... Discriminator Loss: 1.3938... Generator Loss: 0.8794
Epoch 2/25 Step 11700... Discriminator Loss: 1.3480... Generator Loss: 0.7517
Epoch 2/25 Step 11800... Discriminator Loss: 1.2947... Generator Loss: 0.8512
Epoch 2/25 Step 11900... Discriminator Loss: 1.3568... Generator Loss: 0.7912
Epoch 2/25 Step 12000... Discriminator Loss: 1.3167... Generator Loss: 0.7842
Epoch 2/25 Step 12100... Discriminator Loss: 1.3710... Generator Loss: 0.8234
Epoch 2/25 Step 12200... Discriminator Loss: 1.3654... Generator Loss: 0.8936
Epoch 2/25 Step 12300... Discriminator Loss: 1.2843... Generator Loss: 0.8631
Epoch 2/25 Step 12400... Discriminator Loss: 1.3638... Generator Loss: 0.7321
Epoch 2/25 Step 12500... Discriminator Loss: 1.3044... Generator Loss: 1.0416
Epoch 2/25 Step 12600... Discriminator Loss: 1.4268... Generator Loss: 0.8085
Epoch 3/25 Step 12700... Discriminator Loss: 1.3376... Generator Loss: 0.8272
Epoch 3/25 Step 12800... Discriminator Loss: 1.2855... Generator Loss: 0.9438
Epoch 3/25 Step 12900... Discriminator Loss: 1.3198... Generator Loss: 0.8540
Epoch 3/25 Step 13000... Discriminator Loss: 1.3070... Generator Loss: 0.8288
Epoch 3/25 Step 13100... Discriminator Loss: 1.3833... Generator Loss: 1.0739
Epoch 3/25 Step 13200... Discriminator Loss: 1.2858... Generator Loss: 0.9476
Epoch 3/25 Step 13300... Discriminator Loss: 1.3040... Generator Loss: 0.8193
Epoch 3/25 Step 13400... Discriminator Loss: 1.3329... Generator Loss: 0.7463
Epoch 3/25 Step 13500... Discriminator Loss: 1.3230... Generator Loss: 1.0511
Epoch 3/25 Step 13600... Discriminator Loss: 1.3633... Generator Loss: 0.9105
Epoch 3/25 Step 13700... Discriminator Loss: 1.3171... Generator Loss: 0.8048
Epoch 3/25 Step 13800... Discriminator Loss: 1.2508... Generator Loss: 0.9284
Epoch 3/25 Step 13900... Discriminator Loss: 1.3452... Generator Loss: 0.7526
Epoch 3/25 Step 14000... Discriminator Loss: 1.3317... Generator Loss: 1.0048
Epoch 3/25 Step 14100... Discriminator Loss: 1.1132... Generator Loss: 1.0779
Epoch 3/25 Step 14200... Discriminator Loss: 1.0874... Generator Loss: 1.0392
Epoch 3/25 Step 14300... Discriminator Loss: 1.2549... Generator Loss: 0.7551
Epoch 3/25 Step 14400... Discriminator Loss: 1.1508... Generator Loss: 1.0661
Epoch 3/25 Step 14500... Discriminator Loss: 1.3308... Generator Loss: 0.6659
Epoch 3/25 Step 14600... Discriminator Loss: 1.0435... Generator Loss: 1.0125
Epoch 3/25 Step 14700... Discriminator Loss: 1.6895... Generator Loss: 0.4046
Epoch 3/25 Step 14800... Discriminator Loss: 1.3113... Generator Loss: 0.8605
Epoch 3/25 Step 14900... Discriminator Loss: 1.3300... Generator Loss: 0.7830
Epoch 3/25 Step 15000... Discriminator Loss: 1.1570... Generator Loss: 0.8926
Epoch 3/25 Step 15100... Discriminator Loss: 1.2858... Generator Loss: 0.7776
Epoch 3/25 Step 15200... Discriminator Loss: 1.0823... Generator Loss: 1.0453

提交项目

提交本项目前,确保运行所有 cells 后保存该文件。

保存该文件为 "dlnd_face_generation.ipynb", 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。